{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.7.10","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# Importing Libraries","metadata":{}},{"cell_type":"code","source":"! pip install biosppy   ","metadata":{"execution":{"iopub.status.busy":"2021-09-02T18:14:33.410764Z","iopub.execute_input":"2021-09-02T18:14:33.411433Z","iopub.status.idle":"2021-09-02T18:14:45.849556Z","shell.execute_reply.started":"2021-09-02T18:14:33.411334Z","shell.execute_reply":"2021-09-02T18:14:45.848272Z"},"trusted":true},"execution_count":1,"outputs":[{"name":"stdout","text":"Collecting biosppy\n  Downloading biosppy-0.7.3.tar.gz (85 kB)\n\u001b[K     |████████████████████████████████| 85 kB 1.5 MB/s eta 0:00:011\n\u001b[?25hCollecting bidict\n  Downloading bidict-0.21.2-py2.py3-none-any.whl (37 kB)\nRequirement already satisfied: h5py in /opt/conda/lib/python3.7/site-packages (from biosppy) (2.10.0)\nRequirement already satisfied: matplotlib in /opt/conda/lib/python3.7/site-packages (from biosppy) (3.4.2)\nRequirement already satisfied: numpy in /opt/conda/lib/python3.7/site-packages (from biosppy) (1.19.5)\nRequirement already satisfied: scikit-learn in /opt/conda/lib/python3.7/site-packages (from biosppy) (0.23.2)\nRequirement already satisfied: scipy in /opt/conda/lib/python3.7/site-packages (from biosppy) (1.6.3)\nRequirement already satisfied: shortuuid in /opt/conda/lib/python3.7/site-packages (from biosppy) (1.0.1)\nRequirement already satisfied: six in /opt/conda/lib/python3.7/site-packages (from biosppy) (1.15.0)\nRequirement already satisfied: joblib in /opt/conda/lib/python3.7/site-packages (from biosppy) (1.0.1)\nRequirement already satisfied: opencv-python in /opt/conda/lib/python3.7/site-packages (from biosppy) (4.5.2.54)\nRequirement already satisfied: python-dateutil>=2.7 in /opt/conda/lib/python3.7/site-packages (from matplotlib->biosppy) (2.8.1)\nRequirement already satisfied: cycler>=0.10 in /opt/conda/lib/python3.7/site-packages (from matplotlib->biosppy) (0.10.0)\nRequirement already satisfied: pillow>=6.2.0 in /opt/conda/lib/python3.7/site-packages (from matplotlib->biosppy) (8.2.0)\nRequirement already satisfied: kiwisolver>=1.0.1 in /opt/conda/lib/python3.7/site-packages (from matplotlib->biosppy) (1.3.1)\nRequirement already satisfied: pyparsing>=2.2.1 in /opt/conda/lib/python3.7/site-packages (from matplotlib->biosppy) (2.4.7)\nRequirement already satisfied: threadpoolctl>=2.0.0 in /opt/conda/lib/python3.7/site-packages (from scikit-learn->biosppy) (2.1.0)\nBuilding wheels for collected packages: biosppy\n  Building wheel for biosppy (setup.py) ... \u001b[?25ldone\n\u001b[?25h  Created wheel for biosppy: filename=biosppy-0.7.3-py2.py3-none-any.whl size=95409 sha256=0016498210477579d3b1db3026ea2e6932dcc9783ade010a19fa7be17929d217\n  Stored in directory: /root/.cache/pip/wheels/2f/4f/8f/28b2adc462d7e37245507324f4817ce1c64ef2464f099f4f0b\nSuccessfully built biosppy\nInstalling collected packages: bidict, biosppy\nSuccessfully installed bidict-0.21.2 biosppy-0.7.3\n\u001b[33mWARNING: Running pip as root will break packages and permissions. You should install packages reliably by using venv: https://pip.pypa.io/warnings/venv\u001b[0m\n","output_type":"stream"}]},{"cell_type":"code","source":"import numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport tensorflow as tf\nimport wfdb\nimport os                                                                                                         \nimport gc\nimport scipy       \nimport sklearn\nfrom pathlib import Path\nfrom sklearn.utils import shuffle\nfrom sklearn.manifold import TSNE\nimport seaborn as sns\nfrom sklearn import preprocessing\nimport shutil\nimport math\nimport random\nfrom scipy.spatial import distance\nfrom biosppy.signals import ecg\nfrom scipy.interpolate import PchipInterpolator","metadata":{"execution":{"iopub.status.busy":"2021-09-02T18:14:45.851447Z","iopub.execute_input":"2021-09-02T18:14:45.851768Z","iopub.status.idle":"2021-09-02T18:14:54.240952Z","shell.execute_reply.started":"2021-09-02T18:14:45.851732Z","shell.execute_reply":"2021-09-02T18:14:54.239754Z"},"trusted":true},"execution_count":2,"outputs":[]},{"cell_type":"code","source":"try:\n    tpu = tf.distribute.cluster_resolver.TPUClusterResolver()  # TPU detection\n    print('Running on TPU ', tpu.cluster_spec().as_dict()['worker'])\nexcept ValueError:\n    raise BaseException('ERROR: Not connected to a TPU runtime; please see the previous cell in this notebook for instructions!')\n\ntf.config.experimental_connect_to_cluster(tpu)\ntf.tpu.experimental.initialize_tpu_system(tpu)\ntpu_strategy = tf.distribute.experimental.TPUStrategy(tpu)","metadata":{"execution":{"iopub.status.busy":"2021-09-02T18:14:54.242800Z","iopub.execute_input":"2021-09-02T18:14:54.243160Z","iopub.status.idle":"2021-09-02T18:14:59.793799Z","shell.execute_reply.started":"2021-09-02T18:14:54.243117Z","shell.execute_reply":"2021-09-02T18:14:59.792769Z"},"trusted":true},"execution_count":3,"outputs":[{"name":"stdout","text":"Running on TPU  ['10.0.0.2:8470']\n","output_type":"stream"}]},{"cell_type":"markdown","source":"# Dataset Creation","metadata":{}},{"cell_type":"code","source":"####### Dataset Creation \n\n###### Constants\nFS = 500\nW_LEN = 256\nW_LEN_1_4 = 256 // 4\nW_LEN_3_4 = 3 * (256 // 4)\n\n###### Function to Read a Record\ndef read_rec(rec_path):\n\n    \"\"\" \n    Function to read record and return Segmented Signals\n\n    INPUTS:-\n    1) rec_path : Path of the Record\n\n    OUTPUTS:-\n    1) seg_sigs : Final Segmented Signals\n\n    \"\"\"\n    number_of_peaks = 2 # For extracting the required number of peaks                                    \n    full_rec = (wfdb.rdrecord(rec_path)).p_signal[:,1] # Entire Record\n\n    f = PchipInterpolator(np.arange(10000),full_rec) # Fitting Interpolation Function\n    x_samp = (np.arange(10000)*(500/360))[:7200] # Fixing Interpolation Input Values\n    full_rec_interp = f(x_samp)  # Intepolating Values \n    r_peaks_init = ecg.hamilton_segmenter(full_rec_interp,360)[0] # R-Peak Segmentation and input is the signal frequency of 500Hz in this case\n    final_peak_index = r_peaks_init[int(r_peaks_init.shape[0] - int((r_peaks_init.shape[0]%number_of_peaks)))-1]\n    r_peaks_final = r_peaks_init[:final_peak_index] # Final Number of R_Peaks\n    full_rec_final = full_rec_interp[:int(r_peaks_final[-1]+W_LEN)] # Final Sequence\n    seg_sigs, r_peaks_ref = segmentSignals(full_rec_final,list(r_peaks_final)) # Final Signal Segmentation\n\n    return seg_sigs # Returning the Ouput of the Signal Segmentation\n\n###### Function to Segment Signals\n\n##### Function\ndef segmentSignals(signal, r_peaks_annot, normalization=True, person_id= None, file_id=None):\n    \n    \"\"\"\n    Segments signals based on the detected R-Peak\n    Args:\n        signal (numpy array): input signal\n        r_peaks_annot (int []): r-peak locations.\n        normalization (bool, optional): apply z-normalization or not? . Defaults to True.\n        person_id ([type], optional): [description]. Defaults to None.\n        file_id ([type], optional): [description]. Defaults to None.\n    Returns:\n            [tuple(numpy array,numpy array)]: segmented signals and refined r-peaks\n    \"\"\"\n    def refine_rpeaks(signal, r_peaks):\n        \"\"\"\n        Refines the detected R-peaks. If the R-peak is slightly shifted, this assigns the \n        highest point R-peak.\n        Args:\n            signal (numpy array): input signal\n            r_peaks (int []): list of detected r-peaks\n        Returns:\n            [numpy array]: refined r-peaks\n        \"\"\"\n        r_peaks2 = np.array(r_peaks)            # make a copy\n        for i in range(len(r_peaks)):\n            r = r_peaks[i]          # current R-peak\n            small_segment = signal[max(0,r-100):min(len(signal),r+100)]         # consider the neighboring segment of R-peak\n            r_peaks2[i] = np.argmax(small_segment) - 100 + r_peaks[i]           # picking the highest point\n            r_peaks2[i] = min(r_peaks2[i],len(signal))                          # the detected R-peak shouldn't be outside the signal\n            r_peaks2[i] = max(r_peaks2[i],0)                                    # checking if it goes before zero    \n        return r_peaks2                     # returning the refined r-peak list\n    \n    segmented_signals = []                      # array containing the segmented beats\n    \n    r_peaks = np.array(r_peaks_annot)\n\n    r_peaks = refine_rpeaks(signal, r_peaks)\n    skip_len = 2 # Parameter to specify number of r_peaks in one signal\n    max_seq_len = 512 # Parameter to specify maximum sequence length\n    \n    for r_curr in range(0,int(r_peaks.shape[0]-(skip_len-1)),skip_len):\n        if ((r_peaks[r_curr]-W_LEN_1_4)<0) or ((r_peaks[r_curr+(skip_len-1)]+W_LEN_3_4)>=len(signal)):           # not enough signal to segment\n            continue\n        segmented_signal = np.array(signal[r_peaks[r_curr]-W_LEN_1_4:r_peaks[r_curr+(skip_len-1)]+W_LEN_3_4])        # segmenting a heartbeat\n        segmented_signal = list(segmented_signal)\n        #print(segmented_signal.shape)\n        \n        if(len(segmented_signal) < 512):\n            for m in range(int(512-len(segmented_signal))): # Zero Padding\n                segmented_signal.append(0)\n        else:\n            segmented_signal = (segmented_signal[:int(max_seq_len)])\n            \n        segmented_signal = np.array(segmented_signal)\n        \n        if(segmented_signal.shape != (512,1)):    \n            segmented_signal = np.reshape(segmented_signal,(512,1))\n            \n        if (normalization):             # Z-score normalization\n            if abs(np.std(segmented_signal))<1e-6:          # flat line ECG, will cause zero division error\n                continue\n            segmented_signal = (segmented_signal - np.mean(segmented_signal)) / np.std(segmented_signal)            \n              \n        #if not np.isnan(segmented_signal).any():                    # checking for nan, this will never happen\n            segmented_signals.append(segmented_signal)\n\n    return segmented_signals,r_peaks           # returning the segmented signals and the refined r-peaks","metadata":{"execution":{"iopub.status.busy":"2021-09-02T18:14:59.795519Z","iopub.execute_input":"2021-09-02T18:14:59.795827Z","iopub.status.idle":"2021-09-02T18:14:59.819194Z","shell.execute_reply.started":"2021-09-02T18:14:59.795797Z","shell.execute_reply":"2021-09-02T18:14:59.817891Z"},"trusted":true},"execution_count":4,"outputs":[]},{"cell_type":"code","source":"###### Extracting List of the Elements with Two Sessions \ndir = '../input/ecg1d/ecg-id-database-1.0.0'\ntotal_index = 0\nsubjects_with_two = []\n\nfor item in np.sort(os.listdir(dir)):\n    #print('----------------------------------')\n    #print(item)\n    dir_sub = os.path.join(dir,item)\n    if(os.path.isdir(dir_sub)):\n        #print(len(os.listdir(dir_sub))//3)\n        if(len(os.listdir(dir_sub))//3 == 2):\n            total_index = total_index+1   \n            #print(item)\n            subjects_with_two.append(item)\n    #print('----------------------------------')\n\nprint(total_index)\nprint(subjects_with_two)","metadata":{"execution":{"iopub.status.busy":"2021-09-02T13:57:59.001103Z","iopub.execute_input":"2021-09-02T13:57:59.001494Z","iopub.status.idle":"2021-09-02T13:57:59.625358Z","shell.execute_reply.started":"2021-09-02T13:57:59.001458Z","shell.execute_reply":"2021-09-02T13:57:59.624466Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"###### Numpy Array Creation\npath_to_dir = '../input/ecg1d/ecg-id-database-1.0.0'\ntotal_folders = 90\ncurrent_index = 0\n\nX_train = []\nX_dev = []\ny_train = []\ny_dev = []\n\n#for item in subjects_with_two:\nfor i in range(2,92):\n\n    if(i != 75):\n\n        print(i-1)\n        folder_path = os.path.join(path_to_dir,np.sort(os.listdir(path_to_dir))[i]) # Path Selection\n        #items_in_folder = int(len(folder_path)//3)\n        #current_storage_path = './5_Beat_Ecg_ECG1D'+'/person'+str(current_index)\n\n        #for j in os.listdir(item):\n\n        for j in range(2):\n\n            rec_path = folder_path+'/'+'rec'+'_'+str(j+1) # Path to Record\n            seg_signal_current = read_rec(rec_path)\n\n            if(j == 0):\n                for k in range(len(seg_signal_current)):\n                    #file_name_current = current_storage_path+'/'+str(j)+'_/'+str(k)\n                    #np.savez_compressed(file_name_current,seg_signal_current[k])\n                    X_train.append(seg_signal_current[k])\n                    y_train.append(current_index)\n\n            else:\n                for k in range(len(seg_signal_current)):\n                    #file_name_current = current_storage_path+'/'+str(j)+'_/'+str(k)\n                    #np.savez_compressed(file_name_current,seg_signal_current[k])\n                    X_dev.append(seg_signal_current[k])\n                    y_dev.append(current_index)\n\n        current_index = current_index+1\n\n###### Shuffling Numpy Arrays\nX_train,y_train = shuffle(X_train,y_train)\nX_dev,y_dev = shuffle(X_dev,y_dev)\n\n###### Saving Numpy Arrays\nnp.savez_compressed('X_train_ECG1D.npz',np.array(X_train))\nnp.savez_compressed('y_train_ECG1D.npz',np.array(y_train))\nnp.savez_compressed('X_dev_ECG1D.npz',np.array(X_dev))\nnp.savez_compressed('y_dev_ECG1D.npz',np.array(y_dev))  ","metadata":{"execution":{"iopub.status.busy":"2021-09-02T18:14:59.823679Z","iopub.execute_input":"2021-09-02T18:14:59.824084Z","iopub.status.idle":"2021-09-02T18:15:10.399349Z","shell.execute_reply.started":"2021-09-02T18:14:59.824036Z","shell.execute_reply":"2021-09-02T18:15:10.398226Z"},"trusted":true},"execution_count":5,"outputs":[{"name":"stdout","text":"1\n2\n3\n4\n5\n6\n7\n8\n9\n10\n11\n12\n13\n14\n15\n16\n17\n18\n19\n20\n21\n22\n23\n24\n25\n26\n27\n28\n29\n30\n31\n32\n33\n34\n35\n36\n37\n38\n39\n40\n41\n42\n43\n44\n45\n46\n47\n48\n49\n50\n51\n52\n53\n54\n55\n56\n57\n58\n59\n60\n61\n62\n63\n64\n65\n66\n67\n68\n69\n70\n71\n72\n73\n75\n76\n77\n78\n79\n80\n81\n82\n83\n84\n85\n86\n87\n88\n89\n90\n","output_type":"stream"}]},{"cell_type":"code","source":"##### Loading Dataset\nX_train = np.array(np.load('./X_train_ECG1D.npz',allow_pickle=True)['arr_0'],dtype=np.float16)\nX_dev = np.array(np.load('./X_dev_ECG1D.npz',allow_pickle=True)['arr_0'],dtype=np.float16)\ny_train = np.load('./y_train_ECG1D.npz',allow_pickle=True)['arr_0']\ny_dev = np.load('./y_dev_ECG1D.npz',allow_pickle=True)['arr_0']","metadata":{"execution":{"iopub.status.busy":"2021-09-02T18:15:19.710268Z","iopub.execute_input":"2021-09-02T18:15:19.710701Z","iopub.status.idle":"2021-09-02T18:15:19.794423Z","shell.execute_reply.started":"2021-09-02T18:15:19.710660Z","shell.execute_reply":"2021-09-02T18:15:19.793238Z"},"trusted":true},"execution_count":6,"outputs":[]},{"cell_type":"code","source":"##### Converting Labels to Categorical Format\ny_train_ohot = tf.keras.utils.to_categorical(y_train)\ny_dev_ohot = tf.keras.utils.to_categorical(y_dev)","metadata":{"execution":{"iopub.status.busy":"2021-09-02T18:15:21.037138Z","iopub.execute_input":"2021-09-02T18:15:21.037560Z","iopub.status.idle":"2021-09-02T18:15:21.044411Z","shell.execute_reply.started":"2021-09-02T18:15:21.037509Z","shell.execute_reply":"2021-09-02T18:15:21.043216Z"},"trusted":true},"execution_count":7,"outputs":[]},{"cell_type":"markdown","source":"# Model Making","metadata":{}},{"cell_type":"markdown","source":"## Self-Calibrated Convolution","metadata":{}},{"cell_type":"code","source":"###### Model Development : Self-Calibrated \n\n##### Defining Self-Calibrated Block\n\nrate_regularizer = 1e-5\nclass self_cal_Conv1D(tf.keras.layers.Layer):\n\n    \"\"\" \n    This is inherited class from keras.layers and shall be instatition of self-calibrated convolutions\n    \"\"\"\n    \n    def __init__(self,num_filters,kernel_size,num_features):\n    \n        #### Defining Essentials\n        super().__init__()\n        self.num_filters = num_filters\n        self.kernel_size = kernel_size\n        self.num_features = num_features # Number of Channels in Input\n\n        #### Defining Layers\n        self.conv2 = tf.keras.layers.Conv1D(self.num_features/2,self.kernel_size,padding='same',kernel_regularizer=tf.keras.regularizers.l2(rate_regularizer),dtype='float32',activation='relu')\n        self.conv3 = tf.keras.layers.Conv1D(self.num_features/2,self.kernel_size,padding='same',kernel_regularizer=tf.keras.regularizers.l2(rate_regularizer),dtype='float32',activation='relu')\n        self.conv4 = tf.keras.layers.Conv1D(self.num_filters/2,self.kernel_size,padding='same',activation='relu',kernel_regularizer=tf.keras.regularizers.l2(rate_regularizer),dtype='float32')\n        self.conv1 = tf.keras.layers.Conv1D(self.num_filters/2,self.kernel_size,padding='same',activation='relu',kernel_regularizer=tf.keras.regularizers.l2(rate_regularizer),dtype='float32')\n        self.upsample = tf.keras.layers.Conv1DTranspose(filters=int(self.num_features/2),kernel_size=4,strides=4)\n        #self.attention_layer = tf.keras.layers.Attention()\n        #self.lstm = tf.keras.layers.LSTM(int(self.num_features/2),return_sequences=True)\n        #self.layernorm = tf.keras.layers.LayerNormalization()\n    \n    def get_config(self):\n\n        config = super().get_config().copy()\n        config.update({\n            'num_filters': self.num_filters,\n            'kernel_size': self.kernel_size,\n            'num_features': self.num_features\n        })\n        return config\n    \n    \n    def call(self,X):\n       \n        \"\"\"\n          INPUTS : 1) X - Input Tensor of shape (batch_size,sequence_length,num_features)\n          OUTPUTS : 1) X - Output Tensor of shape (batch_size,sequence_length,num_features)\n        \"\"\"\n        \n        #### Dimension Extraction\n        b_s = (X.shape)[0] \n        seq_len = (X.shape)[1]\n        num_features = (X.shape)[2]\n        \n        #### Channel-Wise Division\n        X_attention = X[:,:,0:int(self.num_features/2)]\n        X_global = X[:,:,int(self.num_features/2):]\n        \n        #### Self Calibration Block\n\n        ### Local Feature Detection\n\n        ## Down-Sampling\n        #x1 = X_attention[:,0:int(seq_len/5),:]\n        #x2 = X_attention[:,int(seq_len/5):int(seq_len*(2/5)),:]\n        #x3 = X_attention[:,int(seq_len*(2/5)):int(seq_len*(3/5)),:]\n        #x4 = X_attention[:,int(seq_len*(3/5)):int(seq_len*(4/5)),:]\n        #x5 = X_attention[:,int(seq_len*(4/5)):seq_len,:]\n        x_down_sampled = tf.keras.layers.AveragePooling1D(pool_size=4,strides=4)(X_attention)\n        \n        ## Convoluting Down Sampled Sequence \n        #x1 = self.conv2(x1)\n        #x2 = self.conv2(x2)\n        #x3 = self.conv2(x3)\n        #x4 = self.conv2(x4)\n        #x5 = self.conv2(x5)\n        x_down_conv = self.conv2(x_down_sampled)\n        #x_down_feature = self.attention_layer([x_down_sampled,x_down_sampled])\n        #x_down_feature = self.lstm(x_down_sampled)\n        #x_down_feature = self.layernorm(x_down_feature)\n        \n        ## Up-Sampling\n        x_down_upsampled = self.upsample(x_down_conv)   \n        #X_local_upsampled = tf.keras.layers.concatenate([x1,x2,x3,x4,x5],axis=1)\n\n        ## Local-CAM\n        X_local = X_attention + x_down_upsampled  #X_local_upsampled\n\n        ## Local Importance \n        X_2 = tf.keras.activations.sigmoid(X_local)\n\n        ### Self-Calibration\n\n        ## Global Convolution\n        X_3 = self.conv3(X_attention)\n\n        ## Attention Determination\n        X_attention = tf.math.multiply(X_2,X_3)\n\n        #### Self-Calibration Feature Extraction\n        X_4 = self.conv4(X_attention)\n\n        #### Normal Feature Extraction\n        X_1 = self.conv1(X_global)\n\n        #### Concatenating and Returning Output\n        return (tf.keras.layers.concatenate([X_1,X_4],axis=2))","metadata":{"execution":{"iopub.status.busy":"2021-09-02T18:15:23.621988Z","iopub.execute_input":"2021-09-02T18:15:23.622420Z","iopub.status.idle":"2021-09-02T18:15:23.641493Z","shell.execute_reply.started":"2021-09-02T18:15:23.622381Z","shell.execute_reply":"2021-09-02T18:15:23.640341Z"},"trusted":true},"execution_count":8,"outputs":[]},{"cell_type":"markdown","source":"## Transformer","metadata":{}},{"cell_type":"code","source":"def get_angles(pos, i, d_model):\n    angle_rates = 1 / np.power(10000, (2 * (i//2)) / np.float32(d_model))\n    return pos * angle_rates\n\ndef positional_encoding(position, d_model):\n    angle_rads = get_angles(np.arange(position)[:, np.newaxis],\n                          np.arange(d_model)[np.newaxis, :],\n                          d_model)\n  \n  # apply sin to even indices in the array; 2i\n    angle_rads[:, 0::2] = np.sin(angle_rads[:, 0::2])\n  \n  # apply cos to odd indices in the array; 2i+1\n    angle_rads[:, 1::2] = np.cos(angle_rads[:, 1::2])\n    \n    pos_encoding = angle_rads[np.newaxis, ...]\n    \n    return tf.cast(pos_encoding, dtype=tf.float32)\n\ndef create_padding_mask(seq):\n    seq = tf.cast(tf.math.equal(seq, 0), tf.float32)\n  \n    # add extra dimensions to add the padding\n    # to the attention logits. \n    return seq[:, tf.newaxis, tf.newaxis, :]  # (batch_size, 1, 1, seq_len)\n\ndef scaled_dot_product_attention(q, k, v, mask):\n    \"\"\"Calculate the attention weights.\n    q, k, v must have matching leading dimensions.\n    k, v must have matching penultimate dimension, i.e.: seq_len_k = seq_len_v.\n    The mask has different shapes depending on its type(padding or look ahead) \n    but it must be broadcastable for addition.\n\n    Args:\n    q: query shape == (..., seq_len_q, depth)\n    k: key shape == (..., seq_len_k, depth)\n    v: value shape == (..., seq_len_v, depth_v)\n    mask: Float tensor with shape broadcastable \n          to (..., seq_len_q, seq_len_k). Defaults to None.\n\n    Returns:\n    output, attention_weights\n    \"\"\"\n\n    matmul_qk = tf.matmul(q, k, transpose_b=True)  # (..., seq_len_q, seq_len_k)\n  \n    # scale matmul_qk\n    dk = tf.cast(tf.shape(k)[-1], tf.float32)\n    scaled_attention_logits = matmul_qk / tf.math.sqrt(dk)\n\n    # add the mask to the scaled tensor.\n    if mask is not None:\n        scaled_attention_logits += (mask * -1e9)  \n\n    # softmax is normalized on the last axis (seq_len_k) so that the scores\n    # add up to 1.\n    attention_weights = tf.nn.softmax(scaled_attention_logits, axis=-1)  # (..., seq_len_q, seq_len_k)\n\n    output = tf.matmul(attention_weights, v)  # (..., seq_len_q, depth_v)\n\n    return output, attention_weights\n\nclass MultiHeadAttention(tf.keras.layers.Layer):\n    def __init__(self, d_model, num_heads):\n        super(MultiHeadAttention, self).__init__()\n        self.num_heads = num_heads\n        self.d_model = d_model\n\n        assert d_model % self.num_heads == 0\n\n        self.depth = d_model // self.num_heads\n\n        self.wq = tf.keras.layers.Dense(d_model)\n        self.wk = tf.keras.layers.Dense(d_model)\n        self.wv = tf.keras.layers.Dense(d_model)\n\n        self.dense = tf.keras.layers.Dense(d_model)\n\n    def get_config(self):\n        config = super(MultiHeadAttention, self).get_config().copy()\n        config.update({\n            'd_model': self.d_model,\n            'num_heads':self.num_heads\n        })\n        \n    def split_heads(self, x, batch_size):\n        \n        \"\"\"Split the last dimension into (num_heads, depth).\n        Transpose the result such that the shape is (batch_size, num_heads, seq_len, depth)\n        \"\"\"\n        x = tf.reshape(x, (batch_size, -1, self.num_heads, self.depth))\n        return tf.transpose(x, perm=[0, 2, 1, 3])\n    \n    def call(self, v, k, q, mask):\n        batch_size = tf.shape(q)[0]\n\n        q = self.wq(q)  # (batch_size, seq_len, d_model)\n        k = self.wk(k)  # (batch_size, seq_len, d_model)\n        v = self.wv(v)  # (batch_size, seq_len, d_model)\n\n        q = self.split_heads(q, batch_size)  # (batch_size, num_heads, seq_len_q, depth)\n        k = self.split_heads(k, batch_size)  # (batch_size, num_heads, seq_len_k, depth)\n        v = self.split_heads(v, batch_size)  # (batch_size, num_heads, seq_len_v, depth)\n\n        # scaled_attention.shape == (batch_size, num_heads, seq_len_q, depth)\n        # attention_weights.shape == (batch_size, num_heads, seq_len_q, seq_len_k)\n        scaled_attention, attention_weights = scaled_dot_product_attention(\n            q, k, v, mask)\n\n        scaled_attention = tf.transpose(scaled_attention, perm=[0, 2, 1, 3])  # (batch_size, seq_len_q, num_heads, depth)\n\n        concat_attention = tf.reshape(scaled_attention, \n                                      (batch_size, -1, self.d_model))  # (batch_size, seq_len_q, d_model)\n\n        output = self.dense(concat_attention)  # (batch_size, seq_len_q, d_model)\n\n        return output, attention_weights\n\ndef point_wise_feed_forward_network(d_model, dff):\n    return tf.keras.Sequential([\n      tf.keras.layers.Dense(dff, activation='relu'),  # (batch_size, seq_len, dff)\n      tf.keras.layers.Dense(d_model)  # (batch_size, seq_len, d_model)\n  ])\n\nclass Encoder(tf.keras.layers.Layer):\n    def __init__(self, num_layers, d_model, num_heads, dff,\n               maximum_position_encoding, rate=0.1):\n        super(Encoder, self).__init__()\n\n        self.d_model = d_model\n        self.num_layers = num_layers\n        self.num_heads = num_heads\n        self.dff = dff\n        self.maximum_position_encoding = maximum_position_encoding\n        self.rate = rate\n\n        #self.embedding = tf.keras.layers.Embedding(input_vocab_size, d_model)\n        self.pos_encoding = positional_encoding(maximum_position_encoding, \n                                                self.d_model)\n\n\n        self.enc_layers = [EncoderLayer(d_model, num_heads, dff, rate) \n                           for _ in range(num_layers)]\n\n        self.dropout = tf.keras.layers.Dropout(rate)\n        \n    def get_config(self):\n        config = super(Encoder, self).get_config().copy()\n        config.update({\n            'num_layers': self.num_layers,\n            'd_model': self.d_model,\n            'num_heads':self.num_heads,\n            'dff':self.dff,\n            'maximum_position_encoding':self.maximum_position_encoding,\n            'rate':self.rate  \n        })\n        \n    def call(self, x, training, mask):\n\n        seq_len = tf.shape(x)[1]\n\n        # adding embedding and position encoding.\n        #x = self.embedding(x)  # (batch_size, input_seq_len, d_model)\n        x *= tf.math.sqrt(tf.cast(self.d_model, tf.float32))\n        x += self.pos_encoding[:, :seq_len, :]\n\n        x = self.dropout(x, training=training)         \n\n        for i in range(self.num_layers):\n            x = self.enc_layers[i](x, training, mask)\n\n        return x  # (batch_size, input_seq_len, d_model)\n\nclass EncoderLayer(tf.keras.layers.Layer):\n    def __init__(self, d_model, num_heads, dff, rate=0.1):\n        super(EncoderLayer, self).__init__()\n        \n        self.d_model = d_model\n        self.num_heads = num_heads\n        self.dff = dff\n        self.rate = rate\n\n        self.mha = MultiHeadAttention(d_model, num_heads)\n        self.ffn = point_wise_feed_forward_network(d_model, dff)\n\n        self.layernorm1 = tf.keras.layers.LayerNormalization(epsilon=1e-6)\n        self.layernorm2 = tf.keras.layers.LayerNormalization(epsilon=1e-6)\n\n        self.dropout1 = tf.keras.layers.Dropout(rate)\n        self.dropout2 = tf.keras.layers.Dropout(rate)\n        \n    def get_config(self):\n        config = super(EncoderLayer, self).get_config().copy()\n        config.update({\n            'd_model': self.d_model,\n            'num_heads':self.num_heads,\n            'dff':self.dff,\n            'rate':self.rate  \n        })\n\n    def call(self, x, training, mask):\n\n        attn_output, _ = self.mha(x, x, x, mask)  # (batch_size, input_seq_len, d_model)\n        attn_output = self.dropout1(attn_output, training=training)\n        out1 = self.layernorm1(x + attn_output)  # (batch_size, input_seq_len, d_model)\n\n        ffn_output = self.ffn(out1)  # (batch_size, input_seq_len, d_model)\n        ffn_output = self.dropout2(ffn_output, training=training)\n        out2 = self.layernorm2(out1 + ffn_output)  # (batch_size, input_seq_len, d_model)\n    \n        return out2\n    \nclass Transformer(tf.keras.Model):\n    def __init__(self, num_layers, d_model, num_heads, dff, \n                 pe_input, rate=0.1):\n        super(Transformer, self).__init__()\n        \n        self.num_layers = num_layers\n        self.d_model = d_model\n        self.num_heads = num_heads\n        self.dff = dff\n        self.pe_input = pe_input\n        self.rate = rate\n        \n        self.encoder = Encoder(num_layers, d_model, num_heads, dff, \n                                pe_input, rate)\n        \n    def get_config(self):\n        config = super(Transformer,self).get_config().copy()\n        config.update({\n            'num_layers': self.num_layers,\n            'd_model': self.d_model,\n            'num_heads':self.num_heads,\n            'dff':self.dff,\n            'pe_input':self.pe_input,\n            'rate':self.rate  \n        })\n    \n    def call(self, inp, training, enc_padding_mask):\n        return self.encoder(inp, training, enc_padding_mask)","metadata":{"execution":{"iopub.status.busy":"2021-09-02T18:26:21.166672Z","iopub.execute_input":"2021-09-02T18:26:21.167069Z","iopub.status.idle":"2021-09-02T18:26:21.210427Z","shell.execute_reply.started":"2021-09-02T18:26:21.167035Z","shell.execute_reply":"2021-09-02T18:26:21.209244Z"},"trusted":true},"execution_count":10,"outputs":[]},{"cell_type":"markdown","source":"## ArcFace Loss","metadata":{}},{"cell_type":"code","source":"class ArcFace(tf.keras.layers.Layer):\n    \n    def __init__(self, n_classes, s, m,regularizer):\n        super().__init__()\n        self.n_classes = n_classes\n        self.s = s\n        self.m = m\n        self.regularizer = tf.keras.regularizers.get(regularizer)\n\n    def get_config(self):\n\n        config = super().get_config().copy()\n        config.update({\n            'n_classes': self.n_classes,\n            's': self.s,\n            'm': self.m,\n            'regularizer': self.regularizer\n        })\n        return config\n\n    def build(self, input_shape):\n        super(ArcFace, self).build(input_shape[0])\n        self.W = self.add_weight(name='W',\n                                shape=(input_shape[0][-1], self.n_classes),\n                                initializer='glorot_uniform',\n                                trainable=True\n                                )\n\n    def call(self, inputs):\n        x, y = inputs\n        c = tf.keras.backend.shape(x)[-1]\n        # normalize feature\n        x = tf.nn.l2_normalize(x, axis=1)\n        # normalize weights\n        W = tf.nn.l2_normalize(self.W, axis=0)\n        # dot product\n        logits = x @ W\n        # add margin\n        # clip logits to prevent zero division when backward\n        theta = tf.acos(tf.keras.backend.clip(logits, -1.0 + tf.keras.backend.epsilon(), 1.0 - tf.keras.backend.epsilon()))\n        target_logits = tf.cos(theta + self.m)\n        # sin = tf.sqrt(1 - logits**2)\n        # cos_m = tf.cos(logits)\n        # sin_m = tf.sin(logits)\n        # target_logits = logits * cos_m - sin * sin_m\n        #\n        logits = logits * (1 - y) + target_logits * y\n        # feature re-scale\n        logits *= self.s\n        out = tf.nn.softmax(logits)    \n        return out\n\n    def compute_output_shape(self, input_shape):\n        return (None, self.n_classes)","metadata":{"execution":{"iopub.status.busy":"2021-09-02T18:26:23.379254Z","iopub.execute_input":"2021-09-02T18:26:23.379645Z","iopub.status.idle":"2021-09-02T18:26:23.393858Z","shell.execute_reply.started":"2021-09-02T18:26:23.379612Z","shell.execute_reply":"2021-09-02T18:26:23.392597Z"},"trusted":true},"execution_count":11,"outputs":[]},{"cell_type":"markdown","source":"# Model Training","metadata":{}},{"cell_type":"code","source":"####### Phase-1 Models\n###### Defining Architecture\n\nwith tpu_strategy.scope():\n\n    ##### SC_Module \n\n    #### Defining Hyperparameters\n    num_layers = 2\n    d_model = 512\n    num_heads = 8\n    dff = 1024\n    max_seq_len = 512 #X_train.shape[1]\n    pe_input = 128\n    rate = 0.5\n    num_features = 1\n    num_classes = 5\n\n    #### Defining Layers\n    Input_layer = tf.keras.layers.Input(shape=(max_seq_len,num_features))\n    self_conv1 = self_cal_Conv1D(128,15,128)\n    self_conv2 = self_cal_Conv1D(128,20,128) # Newly Added\n    self_conv3 = self_cal_Conv1D(256,15,128)\n    self_conv4 = self_cal_Conv1D(256,20,256) # Newly Added\n    self_conv5 = self_cal_Conv1D(512,15,256)\n    self_conv6 = self_cal_Conv1D(512,20,512) # Newly Added\n    self_conv7 = self_cal_Conv1D(1024,3,512)\n    self_conv8 = self_cal_Conv1D(1024,5,1024) # Newly Added\n    conv_initial = tf.keras.layers.Conv1D(32,15,padding='same',activation='relu')\n    conv_second = tf.keras.layers.Conv1D(64,15,padding='same',activation='relu')\n    conv_third = tf.keras.layers.Conv1D(128,15,padding='same',activation='relu')\n    #lstm1 = tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(128,activation='tanh',return_sequences=True),merge_mode='ave')\n    transform_1 = tf.keras.layers.Conv1D(128,3,padding='same',kernel_initializer='lecun_normal', activation='selu')\n    transform_2 = tf.keras.layers.Conv1D(256,3,padding='same',kernel_initializer='lecun_normal', activation='selu')\n    transform_3 = tf.keras.layers.Conv1D(512,3,padding='same',kernel_initializer='lecun_normal', activation='selu')\n    transform_4 = tf.keras.layers.Conv1D(1024,3,padding='same',kernel_initializer='lecun_normal', activation='selu')\n    transformer = Transformer(num_layers,d_model,num_heads,dff,pe_input,rate)\n    gap_layer = tf.keras.layers.GlobalAveragePooling1D()\n    arc_logit_layer = ArcFace(5,30.0,0.3,tf.keras.regularizers.l2(1e-4))\n\n    #### Defining Architecture\n    ### Input Layer\n    Inputs = Input_layer\n    Input_Labels = tf.keras.layers.Input(shape=(num_classes,))\n\n    ### Initial Convolutional Layers\n    conv_initial = conv_initial(Inputs)\n    #conv_initial = tf.keras.layers.LayerNormalization()(conv_initial)\n    #conv_initial = tf.keras.layers.MaxPool1D(pool_size=2,strides=2)(conv_initial)     \n    #conv_initial = tf.keras.layers.Add()([conv_initial,Inputs])\n    \n    conv_second = conv_second(conv_initial)\n    #conv_second = tf.keras.layers.LayerNormalization()(conv_second)\n    #conv_second = tf.keras.layers.MaxPool1D(pool_size=2,strides=2)(conv_second)\n    #conv_second = tf.keras.layers.Add()([conv_second,conv_initial])\n    #conv_second = tf.keras.layers.concatenate(axis=2)([conv_initial,conv_second])\n    \n    conv_third = conv_third(conv_second)\n    #conv_third = tf.keras.layers.LayerNormalization()(conv_third)\n    #conv_third = tf.keras.layers.MaxPool1D(pool_size=2,strides=2)(conv_third)\n    #mask = tf.keras.layers.MaxPool1D(pool_size=2,strides=2)(Inputs)\n    #conv_third = tf.keras.layers.Add()([conv_third,conv_second])\n    #conv_third = tf.keras.layers.concatenate(axis=2)([conv_initial,conv_second,conv_third])\n    #conv_third = lstm1(conv_second)\n    #conv_third = tf.keras.layers.Attention()([conv_third,conv_third])\n    \n    ### 1st Residual Block\n    transform_1 = transform_1(conv_third)\n    conv1 = self_conv1(conv_third)\n    #conv1 = tf.keras.layers.AlphaDropout(rate=0.2)(conv1)\n    conv2 = self_conv2(conv1)\n    #conv2 = tf.keras.layers.AlphaDropout(rate=0.2)(conv2)\n    conv2 = tf.keras.layers.Add()([conv2,transform_1])\n    #conv2 = tf.keras.layers.LayerNormalization()(conv2)\n    conv2 = tf.keras.layers.MaxPool1D(pool_size=2,strides=2)(conv2)\n    #mask = tf.keras.layers.MaxPool1D(pool_size=2,strides=2)(mask)    \n\n    ### 2nd Residual Block\n    #conv_third = tf.keras.layers.Attention()([conv_third,conv_third])\n    transform_2 = transform_2(conv2)\n    conv3 = self_conv3(conv2)\n    #conv3 = tf.keras.layers.AlphaDropout(rate=0.2)(conv3)\n    conv4 = self_conv4(conv3)\n    #conv4 = tf.keras.layers.AlphaDropout(rate=0.2)(conv4)\n    conv4 = tf.keras.layers.Add()([conv4,transform_2])\n    #conv4 = tf.keras.layers.LayerNormalization()(conv4)\n    conv4 = tf.keras.layers.MaxPool1D(pool_size=2,strides=2)(conv4)\n    #mask = tf.keras.layers.MaxPool1D(pool_size=2,strides=2)(mask)\n\n    ### 3rd Residual Block\n    transform_3 = transform_3(conv4)\n    conv5 = self_conv5(conv4)\n    #conv5 = tf.keras.layers.AlphaDropout(rate=0.2)(conv5)\n    conv6 = self_conv6(conv5)\n    #conv6 = tf.keras.layers.AlphaDropout(rate=0.2)(conv6)\n    conv6 = tf.keras.layers.Add()([conv6,transform_3])\n    #conv6 = tf.keras.layers.LayerNormalization()(conv6)\n    #conv6 = tf.keras.layers.MaxPool1D(pool_size=2,strides=2)(conv6)\n\n    ### 4th Residual Block\n    #transform_4 = transform_4(conv6)\n    #conv7 = self_conv7(conv6)\n    #conv8 = self_conv8(conv7)\n    #conv8 = tf.keras.layers.Add()([conv8,transform_4])\n\n    ### Transformer\n    ## Wide-Head Attention Model\n    #tx_embedding = tf.keras.layers.Lambda(PE_Layer)(Inputs)\n    #tx_embedding = tf.keras.layers.Dropout(rate)(tx_embedding,training=True)\n    #mask_reshaped = tf.keras.layers.Reshape((max_seq_len,))(Inputs)\n    #encoder_op1 = encoder_block1(tx_embedding,mask_reshaped)\n    #encoder_op2 = encoder_block2(encoder_op1,mask_reshaped)\n\n    ## Narrow-Head Attention Model\n    #mask_reshaped = tf.keras.layers.Reshape((160,))(mask)\n    embeddings =  transformer(inp=conv6,enc_padding_mask=None)\n    #embeddings = transformer(inp=conv6,enc_padding_mask=create_padding_mask(mask))\n    #residual_embeddings = tf.keras.layers.Add()([conv6,embeddings])\n\n    ### Output Layers\n    ## Initial Layers\n    gap_op = gap_layer(embeddings)\n    dense1 = tf.keras.layers.Dense(256,activation='relu')(gap_op)\n    dropout1 = tf.keras.layers.Dropout(rate)(dense1)\n    \n    ## ArcFace Output Network\n    dense2 = tf.keras.layers.Dense(256,kernel_initializer='he_normal',\n                kernel_regularizer=tf.keras.regularizers.l2(1e-4))(dropout1)\n    ##dense2 = tf.keras.layers.BatchNormalization()(dense2)\n    dense3 = arc_logit_layer(([dense2,Input_Labels]))\n    \n    ## Softmax Output Network\n    #dense2 = tf.keras.layers.Dense(256,activation='relu')(dropout1)\n    ###dropout2 = tf.keras.layers.Dropout(rate)(dense2) # Not to be included\n    #dense3 = tf.keras.layers.Dense(35,activation='softmax')(dense2)\n\n    #### Compiling Architecture            \n    ### ArcFace Model Compilation\n    model = tf.keras.models.Model(inputs=[Inputs,Input_Labels],outputs=dense3)\n    ### Softmax Model Compilation\n    #model = tf.keras.models.Model(inputs=Inputs,outputs=dense3)\n    model.load_weights('../input/ecg1d-models/Identification_ECG1D.h5')\n    model.compile(tf.keras.optimizers.Adam(lr=1e-4,clipnorm=1.0),loss='categorical_crossentropy',metrics=['accuracy'])\n\nmodel.summary()      \ntf.keras.utils.plot_model(model)\n##### Model Training \n\n#### Model Checkpointing\nfilepath = './Identification_ECG1D_5.h5'\ncheckpoint = tf.keras.callbacks.ModelCheckpoint(filepath,monitor='val_accuracy',save_best_only=True,mode='max',save_weights_only=True)\n\n#### Custom Learning Rate Schedule\n#def build_lrfn(lr_start=1e-4, lr_max=1e-3, \n#               lr_min=1e-6, lr_rampup_epochs=5, \n#               lr_sustain_epochs=0, lr_exp_decay=.87):\n#    lr_max = lr_max * tpu_strategy.num_replicas_in_sync\n\n#    def lrfn(epoch):\n#        if epoch < lr_rampup_epochs:\n#            lr = (lr_max - lr_start) / lr_rampup_epochs * epoch + lr_start\n#        elif epoch < lr_rampup_epochs + lr_sustain_epochs:\n#            lr = lr_max\n#        else:\n#            lr = (lr_max - lr_min) * lr_exp_decay**(epoch - lr_rampup_epochs - lr_sustain_epochs) + lr_min#\n#        return lr\n#    \n#    return lrfn\n\n#lrfn = build_lrfn()\n#lr_callback = tf.keras.callbacks.LearningRateScheduler(lrfn, verbose=1)\n#callback_list = [checkpoint,  lr_callback]\n\n#### Model Training\n#### Model Training\n### ArcFace Training\n#history = model.fit((X_train,y_train_ohot),y_train_ohot,epochs=500,batch_size=128,\n#                validation_data=((X_dev,y_dev_ohot),y_dev_ohot),validation_batch_size=128,\n#               callbacks=checkpoint)\n\n### Softmax Training \n#history = model.fit(X_train,y_train_ohot,epochs=250,batch_size=128,\n#                validation_data=(X_dev,y_dev_ohot),validation_batch_size=128,\n#                callbacks=checkpoint)\n\n\n##### Plotting Metrics  \n#### Accuracy and Loss Plots \n\n### Accuracy\n#plt.plot(history.history['accuracy'])\n#plt.plot(history.history['val_accuracy'])\n#plt.title('Model Accuracy')\n#plt.ylabel('Accuracy')\n#plt.xlabel('Epoch')  \n#plt.legend(['Train', 'Validation'], loc='best')\n#plt.show()\n\n### Loss     \n#plt.plot(history.history['loss'])  \n#plt.plot(history.history['val_loss'])\n#plt.title('Model Loss')  \n#plt.ylabel('Loss')         \n#plt.xlabel('epoch')\n#plt.legend(['Train', 'Validation'], loc='best')   \n#plt.show()\n\n##### Saving Model            \n#model.save_weights('ECG_SCNRNet.h5' ","metadata":{"execution":{"iopub.status.busy":"2021-09-02T18:26:31.421296Z","iopub.execute_input":"2021-09-02T18:26:31.421672Z","iopub.status.idle":"2021-09-02T18:26:40.227891Z","shell.execute_reply.started":"2021-09-02T18:26:31.421635Z","shell.execute_reply":"2021-09-02T18:26:40.226634Z"},"trusted":true},"execution_count":12,"outputs":[{"name":"stdout","text":"Model: \"model\"\n__________________________________________________________________________________________________\nLayer (type)                    Output Shape         Param #     Connected to                     \n==================================================================================================\ninput_1 (InputLayer)            [(None, 512, 1)]     0                                            \n__________________________________________________________________________________________________\nconv1d_32 (Conv1D)              (None, 512, 32)      512         input_1[0][0]                    \n__________________________________________________________________________________________________\nconv1d_33 (Conv1D)              (None, 512, 64)      30784       conv1d_32[0][0]                  \n__________________________________________________________________________________________________\nconv1d_34 (Conv1D)              (None, 512, 128)     123008      conv1d_33[0][0]                  \n__________________________________________________________________________________________________\nself_cal__conv1d (self_cal_Conv (None, 512, 128)     262464      conv1d_34[0][0]                  \n__________________________________________________________________________________________________\nself_cal__conv1d_1 (self_cal_Co (None, 512, 128)     344384      self_cal__conv1d[0][0]           \n__________________________________________________________________________________________________\nconv1d_35 (Conv1D)              (None, 512, 128)     49280       conv1d_34[0][0]                  \n__________________________________________________________________________________________________\nadd (Add)                       (None, 512, 128)     0           self_cal__conv1d_1[0][0]         \n                                                                 conv1d_35[0][0]                  \n__________________________________________________________________________________________________\nmax_pooling1d (MaxPooling1D)    (None, 256, 128)     0           add[0][0]                        \n__________________________________________________________________________________________________\nself_cal__conv1d_2 (self_cal_Co (None, 256, 256)     385472      max_pooling1d[0][0]              \n__________________________________________________________________________________________________\nself_cal__conv1d_3 (self_cal_Co (None, 256, 256)     1376896     self_cal__conv1d_2[0][0]         \n__________________________________________________________________________________________________\nconv1d_36 (Conv1D)              (None, 256, 256)     98560       max_pooling1d[0][0]              \n__________________________________________________________________________________________________\nadd_1 (Add)                     (None, 256, 256)     0           self_cal__conv1d_3[0][0]         \n                                                                 conv1d_36[0][0]                  \n__________________________________________________________________________________________________\nmax_pooling1d_1 (MaxPooling1D)  (None, 128, 256)     0           add_1[0][0]                      \n__________________________________________________________________________________________________\nself_cal__conv1d_4 (self_cal_Co (None, 128, 512)     1540992     max_pooling1d_1[0][0]            \n__________________________________________________________________________________________________\nself_cal__conv1d_5 (self_cal_Co (None, 128, 512)     5506304     self_cal__conv1d_4[0][0]         \n__________________________________________________________________________________________________\nconv1d_37 (Conv1D)              (None, 128, 512)     393728      max_pooling1d_1[0][0]            \n__________________________________________________________________________________________________\nadd_2 (Add)                     (None, 128, 512)     0           self_cal__conv1d_5[0][0]         \n                                                                 conv1d_37[0][0]                  \n__________________________________________________________________________________________________\ntransformer (Transformer)       (None, 128, 512)     4205568     add_2[0][0]                      \n__________________________________________________________________________________________________\nglobal_average_pooling1d (Globa (None, 512)          0           transformer[0][0]                \n__________________________________________________________________________________________________\ndense_12 (Dense)                (None, 256)          131328      global_average_pooling1d[0][0]   \n__________________________________________________________________________________________________\ndropout_5 (Dropout)             (None, 256)          0           dense_12[0][0]                   \n__________________________________________________________________________________________________\ndense_13 (Dense)                (None, 256)          65792       dropout_5[0][0]                  \n__________________________________________________________________________________________________\ninput_2 (InputLayer)            [(None, 5)]          0                                            \n__________________________________________________________________________________________________\narc_face (ArcFace)              (None, 5)            1280        dense_13[0][0]                   \n                                                                 input_2[0][0]                    \n==================================================================================================\nTotal params: 14,516,352\nTrainable params: 14,516,352\nNon-trainable params: 0\n__________________________________________________________________________________________________\n","output_type":"stream"}]},{"cell_type":"markdown","source":"# Model Testing","metadata":{}},{"cell_type":"markdown","source":"## KNN Testing Based ArcFace Loss","metadata":{}},{"cell_type":"code","source":"###### Testing Model - ArcFace Style\nwith tpu_strategy.scope():     \n\n    def normalisation_layer(x):   \n        return(tf.math.l2_normalize(x, axis=1, epsilon=1e-12))\n\n    #X_dev_flipped = tf.image.flip_up_down(X_dev)  \n    #x_train_flipped = tf.image.flip_up_down(X_train_final)\n\n    predictive_model = tf.keras.models.Model(inputs=model.input,outputs=model.layers[-3].output)\n    predictive_model.compile(tf.keras.optimizers.Adam(lr=1e-4),loss='categorical_crossentropy',metrics=['accuracy'])\n\nwith tpu_strategy.scope():\n    y_in = tf.keras.layers.Input((89,))\n\n    Input_Layer = tf.keras.layers.Input((512,1))\n    op_1 = predictive_model([Input_Layer,y_in])\n\n    ##Input_Layer_Flipped = tf.keras.layers.Input((224,224,3))\n    ##op_2 = predictive_model([Input_Layer_Flipped,y_in]) \n    ##final_op = tf.keras.layers.Concatenate(axis=1)(op_1)\n\n    final_norm_op = tf.keras.layers.Lambda(normalisation_layer)(op_1)\n\n    testing_model = tf.keras.models.Model(inputs=[Input_Layer,y_in],outputs=final_norm_op)\n    testing_model.compile(tf.keras.optimizers.Adam(lr=1e-4),loss='categorical_crossentropy',metrics=['accuracy'])\n\n##### Nearest Neighbor Classification\nfrom sklearn.neighbors import KNeighborsClassifier\nTest_Embeddings = testing_model.predict((X_dev,y_dev_ohot))\nTrain_Embeddings = testing_model.predict((X_train,y_train_ohot))\n\ncol_mean = np.nanmean(Test_Embeddings, axis=0)\ninds = np.where(np.isnan(Test_Embeddings))\n#print(inds)\nTest_Embeddings[inds] = np.take(col_mean, inds[1])\n\ncol_mean = np.nanmean(Train_Embeddings, axis=0)\ninds = np.where(np.isnan(Train_Embeddings))\n#print(inds)\nTrain_Embeddings[inds] = np.take(col_mean, inds[1])\n\n#Test_Embeddings = np.nan_to_num(Test_Embeddings)\n\n##### Refining Test Embeddings\n#for i in range(Train_Embeddings.shape[0]):\n#    for j in range(Train_Embeddings.shape[1]):\n#        if(math.isnan(Train_Embeddings[i,j])):\n#            Train_Embeddings[i,j] == 0\n#        if(Train_Embeddings[i,j]>1e4):\n#            Train_Embeddings[i,j] == 1e4\n\n##### Refining Train Embeddings    \n#for i in range(Test_Embeddings.shape[0]):\n#    for j in range(Test_Embeddings.shape[1]):\n#        if(math.isnan(Test_Embeddings[i,j])):\n#            Test_Embeddings[i,j] == 0\n#        if(Test_Embeddings[i,j]>1e4 or math.isinf(Test_Embeddings[i,j])):\n#            Test_Embeddings[i,j] == 1e4\n\n#del(X_train_final,X_dev,X_dev_flipped,x_train_flipped)\n#gc.collect()\n\nTest_Accuracy_With_Train = []\nTest_Accuracy_With_Test = []\n                                                                     \nfor k in range(1,11):\n    knn = KNeighborsClassifier(n_neighbors=k,metric='euclidean')\n    knn.fit(Train_Embeddings,y_train)\n    Test_Accuracy_With_Train.append(knn.score(Test_Embeddings,y_dev))\n    knn.fit(Test_Embeddings,y_dev)\n    Test_Accuracy_With_Test.append(knn.score(Test_Embeddings,y_dev))\n\nprint('--------------------------------')\nprint(np.max(Test_Accuracy_With_Train))\nprint(np.max(Test_Accuracy_With_Test))\nprint('--------------------------------')\nprint(np.mean(Test_Accuracy_With_Train))\nprint(np.mean(Test_Accuracy_With_Test))\nprint('--------------------------------')\nprint((Test_Accuracy_With_Train)[0])\nprint((Test_Accuracy_With_Test)[0])\nprint('--------------------------------')\n\nplt.plot(np.arange(1,11),np.array(Test_Accuracy_With_Train),label='Test_Accuracy_With_Train')\nplt.plot(np.arange(1,11),np.array(Test_Accuracy_With_Test),label='Test_Accuracy_With_Test')\nplt.title('Testing Accuracy vs Number of Neighbors')\nplt.xlabel('Number of Neighbors')\nplt.ylabel('Test Accuracy')\nplt.legend()       \nplt.show()  \n\nnp.savez_compressed('Test_Embeddngs_ECG1D.npz',Test_Embeddings)\nnp.savez_compressed('Train_Embeddngs_ECG1D.npz',Train_Embeddings)","metadata":{"execution":{"iopub.status.busy":"2021-09-02T12:58:01.634808Z","iopub.execute_input":"2021-09-02T12:58:01.635149Z","iopub.status.idle":"2021-09-02T12:58:15.513044Z","shell.execute_reply.started":"2021-09-02T12:58:01.635115Z","shell.execute_reply":"2021-09-02T12:58:15.512246Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## t-SNE ","metadata":{}},{"cell_type":"code","source":"####### t-SNE Plot Generation\n###### Model Creation\n#with tpu_strategy.scope():          \n#    tsne_model = tf.keras.models.Model(inputs=model.input,outputs=model.layers[-4].output)\n#    tsne_model.compile(tf.keras.optimizers.Adam(lr=1e-4),loss='categorical_crossentropy',metrics=['accuracy'])\n#tsne_model.summary()\n\n###### Model Predicted\n#embeddings_final = tsne_model.predict((X_dev,y_exp))\n\n###### t-SNE plot plotting\n##### Reduction to Lower Dimensions\ntsne_X_dev = TSNE(n_components=2,perplexity=30,learning_rate=10,n_iter=2000,n_iter_without_progress=50).fit_transform(Test_Embeddings)\n\n##### Plotting\nj = 0 # Index for rotating legend\nplt.rcParams[\"figure.figsize\"] = [12,8]\nmStyles = [\".\",\",\",\"o\",\"v\",\"^\",\"<\",\">\",\"1\",\"2\",\"3\",\"4\",\"8\",\"s\",\"p\",\"P\",\"*\",\"h\",\"H\",\"+\",\"x\",\"X\",\"D\",\"d\",\"|\",\"_\",0,1,2,3,4,5,6,7,8,9,10,11,0,1,2,3,4,5,6,7,8,9,10]\nfor idx,color_index,marker_type in zip(list(np.arange(89)),sns.color_palette('muted',47),mStyles):\n    plt.scatter(tsne_X_dev[y_dev == idx, 0], tsne_X_dev[y_dev == idx, 1],marker=marker_type)\n#plt.legend([str(j) for j in range(89)])\nplt.savefig('tsne_plot_5000_iters.png')\nplt.savefig('tsne_plot_5000_iters.pdf')\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2021-06-08T04:02:03.724161Z","iopub.execute_input":"2021-06-08T04:02:03.724631Z","iopub.status.idle":"2021-06-08T04:02:12.484378Z","shell.execute_reply.started":"2021-06-08T04:02:03.724586Z","shell.execute_reply":"2021-06-08T04:02:12.4833Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Dataset Variability Analysis using t-SNE","metadata":{}},{"cell_type":"code","source":"####### Sampling Examples from t-SNE plots\n\n###### Defining Essentials \nX_train_va = [] # va for variabiltiy analysis\nX_dev_va = []\ny_va = [] # Training and Testing Dataset Share the same labels\n\n##### Training Dataset\nfor class_idx in [0,1,2,3,4]: # Selection of Class\n    \n    total_items = 0 # Variable to monitor the Number of Samples in the va set\n    \n    for i in range(X_train.shape[0]): # Looping over the Training Dataset    \n        \n        if(total_items < 5):\n            \n            if(y_train[i] == class_idx):\n                X_train_va.append(X_train[i])\n                y_va.append(class_idx)\n                \n                total_items = total_items+1\n    \n##### Testing Dataset\nfor class_idx in [0,1,2,3,4]: # Selection of Class\n    \n    total_items = 0 # Variable to monitor the Number of Samples in the va set\n    \n    for i in range(X_dev.shape[0]): # Looping over the Training Dataset    \n        \n        if(total_items < 5):\n            \n            if(y_dev[i] == class_idx):\n                X_dev_va.append(X_dev[i])\n                #y_va.appenf(class_idx)\n                \n                total_items = total_items+1","metadata":{"execution":{"iopub.status.busy":"2021-09-02T18:26:47.020973Z","iopub.execute_input":"2021-09-02T18:26:47.021373Z","iopub.status.idle":"2021-09-02T18:26:47.035564Z","shell.execute_reply.started":"2021-09-02T18:26:47.021336Z","shell.execute_reply":"2021-09-02T18:26:47.034388Z"},"trusted":true},"execution_count":13,"outputs":[]},{"cell_type":"code","source":"###### Inferring Data Shape\nprint(np.array(X_train_va).shape)\nprint(np.array(X_dev_va).shape)\nprint(np.array(y_va).shape)","metadata":{"execution":{"iopub.status.busy":"2021-09-02T18:26:50.188058Z","iopub.execute_input":"2021-09-02T18:26:50.188427Z","iopub.status.idle":"2021-09-02T18:26:50.195405Z","shell.execute_reply.started":"2021-09-02T18:26:50.188395Z","shell.execute_reply":"2021-09-02T18:26:50.194073Z"},"trusted":true},"execution_count":14,"outputs":[{"name":"stdout","text":"(25, 512, 1)\n(25, 512, 1)\n(25,)\n","output_type":"stream"}]},{"cell_type":"code","source":"###### Plotting Data \nfor i in range(0,25,5):\n    plt.plot(np.arange(512),X_train_va[i])\n    plt.plot(np.arange(512),X_dev_va[i])\n    plt.show()","metadata":{"execution":{"iopub.status.busy":"2021-09-02T18:26:55.929843Z","iopub.execute_input":"2021-09-02T18:26:55.930257Z","iopub.status.idle":"2021-09-02T18:26:56.680745Z","shell.execute_reply.started":"2021-09-02T18:26:55.930221Z","shell.execute_reply":"2021-09-02T18:26:56.679670Z"},"trusted":true},"execution_count":15,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}},{"output_type":"display_data","data":{"text/plain":"<Figure 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\n"},"metadata":{"needs_background":"light"}},{"output_type":"display_data","data":{"text/plain":"<Figure 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\n"},"metadata":{"needs_background":"light"}},{"output_type":"display_data","data":{"text/plain":"<Figure 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\n"},"metadata":{"needs_background":"light"}},{"output_type":"display_data","data":{"text/plain":"<Figure 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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"###### Creating Numpy Arrays\nnp.savez_compressed('X_train_va_ECG1D.npz',np.array(X_train_va))\nnp.savez_compressed('y_va_ECG1D.npz',np.array(y_va))\nnp.savez_compressed('X_dev_va_ECG1D.npz',np.array(X_dev_va))\n\n##### Loading Dataset\nX_train_va = np.array(np.load('./X_train_va_ECG1D.npz',allow_pickle=True)['arr_0'],dtype=np.float16)\nX_dev_va = np.array(np.load('./X_dev_va_ECG1D.npz',allow_pickle=True)['arr_0'],dtype=np.float16)\ny_va = np.load('./y_va_ECG1D.npz',allow_pickle=True)['arr_0']\n\n##### Converting Labels to Categorical Format\ny_va_ohot = tf.keras.utils.to_categorical(y_va)","metadata":{"execution":{"iopub.status.busy":"2021-09-02T18:27:06.448471Z","iopub.execute_input":"2021-09-02T18:27:06.448867Z","iopub.status.idle":"2021-09-02T18:27:06.465048Z","shell.execute_reply.started":"2021-09-02T18:27:06.448832Z","shell.execute_reply":"2021-09-02T18:27:06.463656Z"},"trusted":true},"execution_count":16,"outputs":[]},{"cell_type":"code","source":"###### Extracting Embeddings\nwith tpu_strategy.scope():     \n\n    def normalisation_layer(x):   \n        return(tf.math.l2_normalize(x, axis=1, epsilon=1e-12))\n\n    #X_dev_flipped = tf.image.flip_up_down(X_dev)  \n    #x_train_flipped = tf.image.flip_up_down(X_train_final)\n\n    predictive_model = tf.keras.models.Model(inputs=model.input,outputs=model.layers[-3].output)\n    predictive_model.compile(tf.keras.optimizers.Adam(lr=1e-4),loss='categorical_crossentropy',metrics=['accuracy'])\n\nwith tpu_strategy.scope():\n    y_in = tf.keras.layers.Input((5,))\n\n    Input_Layer = tf.keras.layers.Input((512,1))\n    op_1 = predictive_model([Input_Layer,y_in])\n\n    ##Input_Layer_Flipped = tf.keras.layers.Input((224,224,3))\n    ##op_2 = predictive_model([Input_Layer_Flipped,y_in]) \n    ##final_op = tf.keras.layers.Concatenate(axis=1)(op_1)\n\n    final_norm_op = tf.keras.layers.Lambda(normalisation_layer)(op_1)\n\n    testing_model = tf.keras.models.Model(inputs=[Input_Layer,y_in],outputs=final_norm_op)\n    testing_model.compile(tf.keras.optimizers.Adam(lr=1e-4),loss='categorical_crossentropy',metrics=['accuracy'])\n\n##### Nearest Neighbor Classification\nfrom sklearn.neighbors import KNeighborsClassifier\nTest_Embeddings = testing_model.predict((X_dev_va,y_va_ohot)) # Session-2 Embeddings\nTrain_Embeddings = testing_model.predict((X_train_va,y_va_ohot)) # Session-1 Embeddings\n\ncol_mean = np.nanmean(Test_Embeddings, axis=0)\ninds = np.where(np.isnan(Test_Embeddings))\n#print(inds)\nTest_Embeddings[inds] = np.take(col_mean, inds[1])\n\ncol_mean = np.nanmean(Train_Embeddings, axis=0)\ninds = np.where(np.isnan(Train_Embeddings))\n#print(inds)\nTrain_Embeddings[inds] = np.take(col_mean, inds[1])","metadata":{"execution":{"iopub.status.busy":"2021-09-02T18:27:16.082066Z","iopub.execute_input":"2021-09-02T18:27:16.082494Z","iopub.status.idle":"2021-09-02T18:27:21.838483Z","shell.execute_reply.started":"2021-09-02T18:27:16.082457Z","shell.execute_reply":"2021-09-02T18:27:21.837312Z"},"trusted":true},"execution_count":17,"outputs":[]},{"cell_type":"code","source":"###### Full Embedding Creation\nEmbeddings = np.array(list(Test_Embeddings)+list(Train_Embeddings))\ny_va_full = np.array(list(y_va)+list(y_va+5))\nprint(y_va_full)","metadata":{"execution":{"iopub.status.busy":"2021-09-02T18:27:22.119682Z","iopub.execute_input":"2021-09-02T18:27:22.120064Z","iopub.status.idle":"2021-09-02T18:27:22.127141Z","shell.execute_reply.started":"2021-09-02T18:27:22.120033Z","shell.execute_reply":"2021-09-02T18:27:22.125994Z"},"trusted":true},"execution_count":18,"outputs":[{"name":"stdout","text":"[0 0 0 0 0 1 1 1 1 1 2 2 2 2 2 3 3 3 3 3 4 4 4 4 4 5 5 5 5 5 6 6 6 6 6 7 7\n 7 7 7 8 8 8 8 8 9 9 9 9 9]\n","output_type":"stream"}]},{"cell_type":"code","source":"###### t-SNE Plotting \n\n##### Testing Dataset - Session 2\n#### Reduction to Lower Dimensions\ntsne_X_dev_va = TSNE(n_components=2,perplexity=30,learning_rate=10,n_iter=2000,n_iter_without_progress=50).fit_transform(Embeddings)\n\n#### Plotting\nj = 0 # Index for rotating legend\nplt.rcParams[\"figure.figsize\"] = [24,12]\nmStyles = ['o','o','o','o','o','s','s','s','s','s'] # Sqaure Session 1 data while Circle Session 2\nfor idx,color_index,marker_type in zip(list(np.arange(10)),['blue','green','orange','red','pink','blue','green','orange','red','pink'],mStyles):\n    plt.scatter(tsne_X_dev_va[y_va_full == idx, 0], tsne_X_dev_va[y_va_full == idx, 1],marker=marker_type,color=color_index,linewidths=4)\nplt.legend([str(j) for j in range(5)])\nplt.legend([str(j-5) for j in range(5,10)])\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2021-09-02T18:27:23.999711Z","iopub.execute_input":"2021-09-02T18:27:24.000070Z","iopub.status.idle":"2021-09-02T18:27:24.858126Z","shell.execute_reply.started":"2021-09-02T18:27:24.000039Z","shell.execute_reply":"2021-09-02T18:27:24.857049Z"},"trusted":true},"execution_count":19,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1728x864 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]}]}